Select language

Objective

"The objective of this project is to revolutionize the way the structure of the proton is accessed, determined, and used in the computation of physical processes at hadron colliders such as the Large Hadron Collider (LHC) of CERN. At a hadron accelerator, predictions require a precise, detailed, and accurate description and understanding of the structure of the colliding protons, as encoded in parton distributions (PDFs) - the distributions of quarks and gluons. At the LHC, PDFs are at present the major source of uncertainty, and in the near future they will be the main hurdle for discovery. The vision of this project is to remove this hurdle by attacking the problem using recent results from artificial intelligence (AI). I will lead a research team of two staff scientists, four postdocs and three PhD students, who will apply to PDF determination the recent methods of deep reinforcement learning and Q-learning, which will be coupled with deep residual networks to achieve a fully parameter- and bias-free understanding of proton structure. I will bring into high-energy physics a methodology so far used for object recognition in self-driving cars and automatic game playing, leading both to new physics, and new computational techniques. The application of these techniques to PDFs will enable me to reach two secondary goals. The first is theoretical: the full use for PDF determination of recent high perturbative order (next-to-next-to leading order or NNLO) computations, which will be integrated by means of a new approximation method which relies on combining known exact results with all-order information in various kinematic limits to extend the scope of the former to a more detailed (""more exclusive"") description of the final state.The second is phenomenological: the integration in PDF determination of the Monte-Carlo event generators which are used to turn field theoretical prediction into a realistic description which may be directly compared to experimental data."

Periodic Reporting for period 1 - NNNPDF (Proton strucure for discovery at the Large Hadron Collider)

"This project addresses the issue of determining the structure of the proton, as probed in high-energy collisions such as the Large Hadron Collider of CERN (LHC). Its main novel aspect consists of systematically using Machine Learning towards this goal. Subsidiary innovations deal with the way to estimate theoretical uncertainties, specifically those related to partial knowledge of the underlying predictions in the theory of strong interactions. The importance of this project for society is both direct and indirect. The direct impact has to do with making progress in our understanding of the fundamental laws of nature. These are currently encoded in a theory, the so-called standard model of fundamental interactions, which is tested experimentally at particle accelerators such as the LHC. Whereas no deviation between experimental data and the predictions of this theory has ever been observed, we know that it cannot be complete because, for example, it cannot account for dark matter which makes up about 85% of matter in the universe. The structure of the proton is determined by the strong force, one of the four fundamental forces of Nature, which is described within the standard model by the theory of quantum chromodynamics. Understanding the structure of the proton simultaneously probes our understanding of this theory, and also it enables the subtle tests of the standard model at the LHC which are currently our best way of going beyond the current theory. Indeed, because the LHC is a proton accelerator, no discovery is possible at the LHC without a detailed understanding of proton structure. For instance, the then best understanding of proton structure was crucial in the discovery of the Higgs boson in 2012. The techniques developed in this project are aimed at making possible discoveries which go beyond this and which will be the focus of experimentation at the LHC over the next two decades. The indirect impact has to do with the project methodology, namely the use of machine learning methods. Machine learning techniques are becoming ubiquitous in a variety of applications which go from speech recognition to self-driving cars. In all these situations, machine learning tools are used to determine a true answer from fuzzy information. In the context of this project instead, what is being determined from fuzzy information is a statistical distribution of true answers. This is due to the quantum nature of the objects being studied, which can only be characterized in terms of probability distributions. These techniques are likely to be useful for situations in which instead of a unique correct answer there exists a distribution of possible answers. The overall objectives of the project are the development of a suite of machine learning tools which can achieve a full determination of the proton structure, while optimizing automatically the way information is extracted from the underlying data. This is to be coupled with the development of a set of theoretical results specific to quantum chromodynamics which will lead to accurate estimates of missing theoretical predictions based on optimal exploitation of the information contained in partial results, some of which will be obtained specifically in this project, and all of which will be systematized.In a first phase the overall objectives consist of singling out the specific machine learning tools which allow for automatic optimization of the determination of proton structure, and of classifying mathematical properties of partial results (""resummation"") which allow for the approximate determination of yet unknown corrections to higher order theoretical predictions."